Inspired by the successful application of CNN in SISR, many learning-based methods have been developed to implicitly learn LF geometric structures to enhance the resolution of LF images. Yoon et al. [1]}, [2]} proposed the pioneering method LFCNN to enhance the angular and spatial resolution simultaneously in which the spatial resolution of each SAI was enhanced by SRCNN [3]}. Wang et al. [4]} modeled spatial relations between horizontally or vertically neighbouring views by using a bidirectional recurrent CNN and then super-resolved LF images with stacked generalization technique. Cheng et al. [5]} combined the internal similarity and the external correlation in LFs to enhance the image resolution. Yeung et al. [6]} constructed an alternate spatial-angular convolution to extract LF structural information with a single forward pass. Zhang et al. [7]} employed four stacked SAIs along different angular directions into a multi-stream residual network (resLF) to super-resolve LF images. Wang et al. [8]} proposed an LF-InterNet to decouple the LF structural information into angular and spatial information for LF image SR. They further proposed to use deformable convolutions to address the disparity issue among LF images, and proposed an LF-DFnet [9]} for LF image SR. Meng et al. [10]} developed a 4D CNN to fully use the LF information from all adjacent SAIs. Jin et al. [11]} cascaded two modules (i.e. an all-to-one (ATO) module and a structural consistency regularization module) for LF image SR.
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